Adaptive Local Modularity Learning for Efficient Multilayer Graph Clustering

Existing multilayer graph clustering models focus on integrating the monolithic structure of each layer, but the local preference between layers and clusters has not been fully exploited. To alleviate this problem, this paper proposes a novel multilayer graph clustering model with Adaptive Local Modularity Learning (ALML), which mines truncated layer-cluster relationships adaptively with graph modularity learning paradigm, to reveal the local preference in multilayer graphs. More importantly, ALML reveals the equivalence between graph cut and graph modularity learning in multilayer graph scenarios in theory. To solve the optimization problem involved in ALML, this paper proposes an efficient alternating algorithm with quadratic-level time complexity, which is satisfactory in multilayer graph clustering scenarios, and provides corresponding analyses. In the simulations, extensive experimental results demonstrate that ALML outperforms state-of-the-art competitors in terms of effectiveness as well as efficiency.